{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure and subplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fg= plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ax1=fg.add_subplot(2,2,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ax2=fg.add_subplot(2,2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ax3=fg.add_subplot(2,2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy.random import randn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110efa590>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax3.plot(randn(50).cumsum(),'b+-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   3.,   7.,   3.,   7.,   6.,   6.,   6.,  10.,   6.,  11.,\n",
       "          6.,   9.,   7.,   3.,   4.,   1.,   1.,   1.,   2.]),\n",
       " array([-2.31640787, -2.07625187, -1.83609588, -1.59593988, -1.35578388,\n",
       "        -1.11562788, -0.87547188, -0.63531588, -0.39515988, -0.15500388,\n",
       "         0.08515212,  0.32530812,  0.56546411,  0.80562011,  1.04577611,\n",
       "         1.28593211,  1.52608811,  1.76624411,  2.00640011,  2.24655611,\n",
       "         2.48671211]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax1.hist(randn(100),bins=20,color ='k',alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1111a0390>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax2.scatter(np.arange(30),np.arange(30)+3*randn(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GnFui+KQEsp2W0CLIyqYEokiam5tpaWlJe3+PHj3o3bt32vsL9RxdSUtLS7sLhPT9Kj53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\nCDMzM+vZ38cLriUMP9/EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f871d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig,axes=plt.subplots(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11120c150>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1113104d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111394350>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1113f5910>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111479890>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x111419190>]], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fg.subplots_adjust(left=None,bottom=None,right=None,top=None,wspace=None,hspace=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ssm9qaoqpqamm69QeZmZmhpmZmV327dixY75p5lFLwjyqbRpmsrYojpItcHDx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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11126a4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig,axes=plt.subplots(2,2,sharex=True,sharey=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ah4CfAM8ApwFXAXc55x4KL2wRERGpR9AzABeR/9/8nb77LwBuAKbJ9wf4FLAM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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111655150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        axes[i,j].hist(randn(500),bins=50,color='r',alpha=0.5)\n",
    "plt.subplots_adjust(wspace=0,hspace=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 标记，颜色，线型 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig=plt.figure();ax=fig.add_subplot(1,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x111e92410>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(randn(1000).cumsum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x111ae8790>,\n",
       " <matplotlib.text.Text at 0x111c7a390>,\n",
       " <matplotlib.text.Text at 0x111e92ed0>,\n",
       " <matplotlib.text.Text at 0x111e9e390>,\n",
       " <matplotlib.text.Text at 0x111e9ead0>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.set_xticks([0,250,500,750,1000])\n",
    "ax.set_xticklabels(['one','two','three','four','five'],rotation=30,fontsize='small')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SmNrXl/vnMHOmtG+8UXpBoYGOcQsKRUXWunXyeeYazW4LCts9RUipWL5c2scf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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111def510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1120a6650>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = plt.figure();ax = fig.add_subplot(1,1,1)\n",
    "ax.plot(randn(1000).cumsum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1120a6890>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(randn(1000).cumsum(),'k',label='two')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1120b4510>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(randn(1000).cumsum(),'k.',label='three')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x111925ad0>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(randn(1000).cumsum(),'k--',label='one')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1120b4f90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.text(10,10,'Hello world!',family='monospace',fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 在plot上进行注解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "data = pd.read_csv('./data/pydata-book/ch08/spx.csv',index_col=0,parse_dates=True)\n",
    "spx =data['SPX']\n",
    "spx.plot(ax=ax,style='k-')\n",
    "crisis_data = [\n",
    "    (datetime(2007,10,11),'Peak of bull market'),\n",
    "    (datetime(2008,3,12),'Bear Stearns Fails'),\n",
    "    (datetime(2008,9,15),'Lehman Bankruptcy')\n",
    "]\n",
    "for date,label in crisis_data:\n",
    "    ax.annotate(label,xy=(date,spx.asof(date)+50),xytext=(date,spx.asof(date)+200),\n",
    "               arrowprops=dict(facecolor='black'),horizontalalignment='left',verticalalignment='top')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(732677.0, 734138.0)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.set_xlim(left='1/1/2007',right='1/1/2011')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(600, 1800)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.set_ylim([600,1800])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115029fd0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.set_title(['Important dates in 2008-2009 financial crisis'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AQIDbOpNDRHj11VfZsmULtWvXZsKECUyZMoVHHnnEyjN79myuX79O3bp1eeWV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\nJEmShi4ZIJIkSZIkDV3/B8V2VHP8BE2dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114fe8d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Polygon at 0x117482390>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "ax=fig.add_subplot(1,1,1)\n",
    "\n",
    "rect = plt.Rectangle((0.2,0.75),0.4,0.15,color = 'k',alpha=0.3)\n",
    "circ = plt.Circle((0.7,0.2),0.15,color='b',alpha=0.3)\n",
    "pgon = plt.Polygon([[0.15,0.15],[0.35,0.4],[0.2,0.6]],color='g',alpha=0.5)\n",
    "\n",
    "ax.add_patch(rect)\n",
    "ax.add_patch(circ)\n",
    "ax.add_patch(pgon)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.savefig('fig-patch.svg');plt.savefig('fig-patch.png',dpi=400,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot in pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pandas import DataFrame,Series\n",
    "import numpy as np\n",
    "S = Series(np.random.randn(10).cumsum(),index=np.arange(0,100,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117690450>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.plot(figsize=(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZZ8KHH0KvXmFXI/msoqKCwsJCgELnXEVjrhXYPUfn3GJgDnCPmR0eD7lxQHki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\nTat4zE86pqlzbkb875udcyc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1176abfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.188491</td>\n",
       "      <td>-1.194392</td>\n",
       "      <td>-1.035298</td>\n",
       "      <td>-1.783681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-1.494446</td>\n",
       "      <td>-1.322010</td>\n",
       "      <td>0.137764</td>\n",
       "      <td>1.593909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.772037</td>\n",
       "      <td>0.710650</td>\n",
       "      <td>-0.862620</td>\n",
       "      <td>-0.225157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-1.538718</td>\n",
       "      <td>-1.155522</td>\n",
       "      <td>-0.039674</td>\n",
       "      <td>-0.146174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>-0.681899</td>\n",
       "      <td>-1.850561</td>\n",
       "      <td>-0.281847</td>\n",
       "      <td>1.601941</td>\n",
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       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.154110</td>\n",
       "      <td>-0.782980</td>\n",
       "      <td>-1.691306</td>\n",
       "      <td>-3.265534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.604148</td>\n",
       "      <td>2.500559</td>\n",
       "      <td>1.301293</td>\n",
       "      <td>0.903041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.291366</td>\n",
       "      <td>-0.068547</td>\n",
       "      <td>-0.510011</td>\n",
       "      <td>0.148379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>-0.927647</td>\n",
       "      <td>-0.205778</td>\n",
       "      <td>-2.092972</td>\n",
       "      <td>-1.869148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>-2.018019</td>\n",
       "      <td>-1.656449</td>\n",
       "      <td>-0.333004</td>\n",
       "      <td>0.086736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A         B         C         D\n",
       "0  -1.188491 -1.194392 -1.035298 -1.783681\n",
       "10 -1.494446 -1.322010  0.137764  1.593909\n",
       "20  1.772037  0.710650 -0.862620 -0.225157\n",
       "30 -1.538718 -1.155522 -0.039674 -0.146174\n",
       "40 -0.681899 -1.850561 -0.281847  1.601941\n",
       "50  0.154110 -0.782980 -1.691306 -3.265534\n",
       "60  0.604148  2.500559  1.301293  0.903041\n",
       "70  0.291366 -0.068547 -0.510011  0.148379\n",
       "80 -0.927647 -0.205778 -2.092972 -1.869148\n",
       "90 -2.018019 -1.656449 -0.333004  0.086736"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= DataFrame(np.random.randn(10,4).cumsum(1),columns=['A','B','C','D'],index=np.arange(0,100,10));df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.248101</td>\n",
       "      <td>-0.676061</td>\n",
       "      <td>-1.274724</td>\n",
       "      <td>0.573424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.859225</td>\n",
       "      <td>1.130035</td>\n",
       "      <td>0.159611</td>\n",
       "      <td>-0.183367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.250821</td>\n",
       "      <td>-0.886662</td>\n",
       "      <td>0.573335</td>\n",
       "      <td>-0.107572</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.278811</td>\n",
       "      <td>-0.537998</td>\n",
       "      <td>-0.404999</td>\n",
       "      <td>-0.265150</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.461355</td>\n",
       "      <td>-0.208411</td>\n",
       "      <td>0.039415</td>\n",
       "      <td>-0.574448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>-0.388519</td>\n",
       "      <td>-0.225210</td>\n",
       "      <td>-1.013206</td>\n",
       "      <td>1.595052</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.191051</td>\n",
       "      <td>2.073629</td>\n",
       "      <td>0.209167</td>\n",
       "      <td>0.093217</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.221083</td>\n",
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       "      <td>-0.948528</td>\n",
       "      <td>-0.481334</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.532823</td>\n",
       "      <td>0.843200</td>\n",
       "      <td>1.628244</td>\n",
       "      <td>1.863036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "          0         1         2         3\n",
       "0  0.248101 -0.676061 -1.274724  0.573424\n",
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       "2  0.250821 -0.886662  0.573335 -0.107572\n",
       "3  0.278811 -0.537998 -0.404999 -0.265150\n",
       "4  0.461355 -0.208411  0.039415 -0.574448\n",
       "5 -0.388519 -0.225210 -1.013206  1.595052\n",
       "6  0.191051  2.073629  0.209167  0.093217\n",
       "7  0.221083  0.427017 -0.948528 -0.481334\n",
       "8 -0.505664 -0.302843  0.151416  0.481192\n",
       "9 -0.532823  0.843200  1.628244  1.863036"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = DataFrame(np.random.randn(10,4));a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b=a.cumsum(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117545490>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rNbidfBvTvazQqBpsilSq0WBD+eZBN4qL8VqdOvixSRO8ZG//2Gt9nXzh6+SL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117df43d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 柱状图 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1132b6cd0>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(2,1)\n",
    "data =Series(np.random.rand(16),index=list('abcdefghijklmopq'))\n",
    "data.plot(kind='bar',ax=axes[0],color='k',alpha=0.7)\n",
    "data.plot(kind='barh',ax=axes[1],color ='k',alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FvZ5n4ZEJj3OSMoB9ql7kmGOOYfHixW3rlCRJ/aRv8izsjZ07d3Y8AZPJYiRJ\nqtaVYKHcMRFTHVdXUiaTxUiS1FgngoXJrvcw3bLd1JWUya2TkiQ11vZgITOXT1J21CSH/nlmXl31\nWiZlkiSp+2b0moW6kjJJM4WzXJJmohkdLNSVlEmaKUwQJmkmmtHBQh1JmaSZwgRhkmaqGR0s1JWU\nSZopTBAmqRm9npSpLZYsWcKiRYv2KO907gWpG8z5IalZJmWivjwL0kxhzg9JM1EtwUKZhOnmzDyn\nmXp15VmQZgp3Q0iaibo1szBlQiYwz4IkSTNBV4KFyRI3SZKkmalb14Z4MbAWOCszhxodZ1ImSb3A\n00fqd7UHCxGxErgMGMzMf6w61qRMknqBybTU72oNFiLiTcD7gZdm5r9NdbxJmSTNdCbT0mxQZ7Dw\nCuBQ4DmZedN0KpiUSVIvMJmWuqUfkzLdDCwFzgCmFSyYlEnSTGcyLXVTPyZl2gj8v8C/RsSjwEHA\ngsx8eaMKJmWS1AtMpqV+V+uahcz8UUT8JnAd8AXgLVXHm5RJUi9wN4T6XV3Bwn8nYcrMH0bECoqA\nYQfw9kaVTMokSVL31RIsTEzClJm3RcQ/AUYCkiTNcDP6QlImZVLdnE6WpD3N6GDBpEyqm8l1JGlP\nMzpYMCmT6mRyHUma3IwOFkzKpLqZXEdSL+nHpExNMymT6mRyHUm9ph+TMv23iLgOOAT4YdVxJmVS\n3UyuI0l76tbMwinAR4GBqoNMyqS6uRtCkvbUlWAhMx+IiH2Bn1UdZ1ImSZK6r/ZgoQwSvgU8Dfhg\n1bHmWehv/oqXpN7QjZmFZwDPBu4ALq860DwL/c2cBpLUG2oPFjLz1oj4OnBzZlbu7zDPQv8yp4Ek\n9Y4ZvXVy7ty5DAxUroFUDzOngST1hhkdLGzYsIHNmzfvVmZSJkmSCiZlwqRM/cwESJK09/omKVOZ\ngOnmzDyn2bomZepvJkCSpN7QrZmFnM5BJmXqb26dlKTe0K2kTMunc5xJmSRJ6r66goV9IuIC4A3A\nw8DlmfmnU1UyKZMkqR/12sxqXcHCacAlwK8CvwZ8OiK+kZlfrapkUiZJUj/qtaR0dQUL38nM95X3\nN0bEm4EVQGWwYFImSVK/6cWkdLUFCxMe/wQ4dKpKmzZtYuvWrbuVmWdBktTr2pWUrt/yLDwy4XEC\n+0xV6ZhjjmHx4sWd6ZEkST2ub/Is7I2dO3eagEmS1Fd6MSndjA4WTMokSepHvZaUro5gYVoJmCZj\nUiZJUj8R9P6+AAAMh0lEQVRy6+QEkyVgysxTplPXpEySJHVfLachIuIg4ArgZcA24MLyfuU1I0zK\nJEmqU6/94q9LXWsWVgMnAS8BtgLvA5YCN1dVMimTJKlOvZYsqS51XHXyIOB1wKsy82tl2euBu6eq\na1ImSVJdejFZUl3qmFk4qmzn38cKMnM0In4wVUWTMkmS6tSuZEl16aekTFH+d+KuiJh44ERLlixh\n0aJFe5Sbe0GS1G69mP+gn5IybQR2UVxE6gsAETEfOAb4WlVF8yxIkurUa/kP6lLH1smfRcRVwEUR\ncT8wDHwZGADeGhGfzsyJ144AzLMgSaqXuyEmV9duiFXA5RRBwk5gAfB94CvAdxtVMs+CJEndN+XF\nnNohM7dn5msz8xeA/w3cBRxOcenqx+rogyRJak1dSZl+BXga8LvlbWxx43nApxrVMymT1H+c5pV6\nT50Xknob8FRgB8WMxm8Bt1VVMCmT1H9MeiP1nlqChcy8BXg2QES8BXhLZn5zqnomZZL6i0lvpN40\noy9RbVImqf/0WtIbaSbrp6RMLTMpk9RfejHpjTST9VNSppaZlEnqPya9kXrPjA4WTMok9R93Q0i9\nZ0YHCyZlkiSp+2pJyhSFd0fEj4EPAdsi4nfraFuSJO2dumYWzgVWAmcCPwJ+A/iriNiamf/WqJJJ\nmSRJ6v7pu44HCxGxP/BuYEVmfrssviMingv8L6BhsGBSJkmSup/MrI6ZhacABwJfiYgYVz4HuLmq\nokmZJEmz3UxIZlZHsHBQ+d8XAXdPeK4yO4tJmSRJapzMrJ+SMn2fIih4cmZ+o5mKJmWSJM12VcnM\n+iYpU2b+LCIuAlZHxL7AN4AFwGeBjZn5wkZ1TcokSVL3k5nVdSGpP46ILcC7gKOAByjWLKyrqmdS\nJkmSZsFuiDGZ+RfAX4w9jojr2HMNw25MyiRJUvfVEixExIHA5cApwChw8XTqmWehHt2OWCVJM1td\nMwsXAc8FXgoMU2RxXMYUWyfNs1CPbu/flSTNbHUkZRoATgdWZubXyrLTgDunqmuehc6bCft3JUkz\nWx0zC0dTLGa8cawgM++PiB9MVXHu3LkMDAx0sm+i8f5dSZKgnmBhLGtjNltxw4YNbN68ebcykzJJ\nklTop6RMPwJ2AScC/xcgIh4HPBX4WlVFkzJ1XlWyD0nSzNZPSZm2R8SVwIURcR/FAsf3A49OVdek\nTPXodrIPSdLMVtduiLcDA8DVwE8ptk5O+e1kUqZ6uHVSklSlrgyO24HTytuYKXMtmJRJkqTuqy2D\nY3l56rcDbwSOAO4BrsjMDzWqM92kTP4yliSpc2oLFoA/A84A3gp8E3gC8LSqCtNNymRSIUmSOqeu\ndM8HAX8EvCkzP1MW3w58q6redJIymVRIkqTOqmtm4Thgf+Bfmqm0adMmtm7dulvZZHkWTCokSZqN\n+inPAkBLm/mPOeYYFi9e3O6+SJLUF/omz0JpA/AQsAL4y+lW2rlz55QJmEwqJElSZ9VxIanrKK4u\neQHw4Yh4hGKB4yHAL2Vmw+BhukmZTCokSVLn1LYbIjPPLwOFPwWeCPwEuLyqznSTMrl1UpKkzqlz\n6yRlToWGeRUmMimTJEndV1ewsF9EfBR4LfAI8LHM/JOpKk03KZPax1kaSdJEdQULvw98EjgBeDbw\niYj4r8y8sqrSdJMyqX1McCVJmqiuYGFTZp5T3t8QEc8EVgGVwcJ0kjKpfUxwJUmaTF3Bwg0THl8P\nnBMRkZnZqNJ0kzKpfUxwJUm9o9+SMrVkyZIlLFq0aI/yqXIvqDXmrJCk3tJvSZlOnPD4JGBD1awC\nTD/PgtrHnBWSpInqChaOiIiLgI8Dy4A3U6xZqDTdPAtqH3dDSJImqiNYSGANMA+4EdgFrM7MT05V\n0TwLkiR1X8eDhcxcPu7h2Z1uT5IktVctpyEi4mTgvcAzgEcpdkO8JTN/XFXPpEySpFZ4SrW96lqz\nMABcDHwHOAg4H/gCcHxVJZMy1eu+++7j8Y9/fLe7Mas45vVzzOvXjTE3wVx71RIsZObnxz+OiDcC\nWyLi6Zn5/Ub1TMpUr02bNnHUUUd1uxuzimNeP8e8fnWPuQnm2q+u0xBPoZhN+B/AImAfioWPS4CG\nwYJJmeq13377MTAw0O1uzCqOef0c8/p1Y8xnS4K5fkvK9PfA7cAbgLspgoXvAftXVTIpU7127drl\n2NbMMa+fY16/usd8NiWY65ukTBHxeOCpwBmZ+c2y7NenU9ekTPV65JFHHO+aOeb1c8zr140xN8Fc\ne9Uxs3A/MAKcGRH3AE8GPkRxGqKRAwDOPPNM8yzU6AMf+ADvec97ut2NWcUxr59jXr9ujPlBBx3E\n5s2b2bx5c63tzgTr168fu3tAu14zpsi43J5GIpYDlwJHAT8A/gj4GnBKZl49yfErgb/ueMckSepf\nr87Mte14oVqChWZFxELgZOAO4KHu9kaSpJ5yAHAkcE1mjrTjBWdksCBJkmaOfbrdAUmSNLMZLEiS\npEoGC5IkqZLBgiRJqmSwIEmSKnUlWIiIsyPi9ojYERE3RMQJUxz/yohYXx5/a0S8sK6+9otmxjwi\n3hARX4+I+8rbV6b6N9Kemn2fj6v3qoh4LCI+P/XRGq+Fz5YFEfF/IuLuss5tEfE/6+pvP2hhzN9a\njvODEbEpIi6JiLl19bfXRcRzI+LqiLir/Jz4nWnUeX5E3BQRD0XEDyPitGbbrT1YiIhTKS5XfR7w\nLOBW4JqI2PMiEMXxJwFrgU8AvwJ8EfhiRDy9nh73vmbHHHgexZg/HzgR2Az8c0Q8ofO97Q8tjPlY\nvScDFwJf73gn+0wLny1zgGspLmj3cuBY4I3AXbV0uA+0MOYrKTL4ngc8DTgdOBX4QC0d7g8DwC3A\n2VRnQgYgIo6kuD7TV4HjgY8An4yI326q1cys9QbcAHxk3OMA7gTe0eD4vwGunlB2PXBZ3X3v1Vuz\nYz5J/X2AbcBruv239MqtlTEvx/nfgNcDnwI+3+2/o5duLXy2nAVsAPbtdt979dbCmH8U+MqEsouA\nr3f7b+nFG/AY8DtTHHMB8J0JZUPAPzbTVq0zC2Ukv4wiwgEgi55fC5zUoNpJ5fPjXVNxvMZpccwn\nGgDmAPe1vYN9aC/G/Dxga2Z+qrM97D8tjvlLKX94RMQ9EfGfEfHuiHAt1zS0OObfApaNnaqIiKOA\nFwH/0Nnezmon0obv0LouUT1mEbAvsGVC+RaKKcDJLG5w/OL2dq1vtTLmE11AMTU78Q2nyTU95hHx\nHIoZheM727W+1cr7/ChgOfAZ4IXAMcBl5eu8vzPd7CtNj3lmDpWnKL4REVHWvzwzL+hoT2e3Rt+h\n8yNibmbunM6L1B0sNBJM49zLXhyvPU1rDCPiXcDvAc/LzIc73qv+NumYR8RBwF8Bb8zM+2vvVX+r\nep/vQ/GheWb5i/jmiDgceBsGC3uj4ZhHxPOBcylOAd0IPAW4NCJ+kpmOeX2i/O+0v0frDhbuBR4F\nDptQfih7Rj5j7mnyeO2ulTEHICLeBrwDWJGZ3+tM9/pSs2N+NMWl279c/tqCcvFxRDwMHJuZt3eo\nr/2ilff5T4CHy0BhzHpgcUTsl5m72t/NvtLKmJ8PrBl3qu17ZbB8BQZondLoO3S0mR+AtZ6by8xH\ngJuAFWNl5YfjCopzWZO5fvzxpd8uyzWFFseciHg78B7g5My8udP97CctjPl64JcpdvscX96uBv6l\nvL+5w13ueS2+z79J8ct2vGOBnxgoTK3FMT+QYlHeeI+VVWOS47X3JvsOfQHNfod2YfXm7wE7gNdR\nbJ25AhgBDimfXwN8cNzxJwEPA+dQ/I/8vykuW/30bq9E7ZVbC2P+jnKMT6GISMduA93+W3rl1uyY\nT1Lf3RAdHnPgSRS7fD5CsV7hxRS/wt7V7b+lV24tjPl5wAMU2yWPpPjhtwFY2+2/pVduFAvOj6f4\ncfEY8Nby8RHl8x8Crhp3/JHAzyjWnh0LvKn8Tv2tZtqtfc1CZn6uXOByPsUX0C0Uv16Hy0OeBOwa\nd/z1ETFIsQ/3AxRvrJdl5vfr7XnvanbMgT+g2P3wdxNe6k/L19AUWhhz7aUWPlvujIgXAKsp8gPc\nVd7/cK0d72EtvM/fR/EF9z7gcGCYYhbtvbV1uvc9G7iOYr1BUuS5ALiKIm/FYuCIsYMz846IeDFw\nCfBHFFtbz8jMphasRxl5SJIkTcr9xJIkqZLBgiRJqmSwIEmSKhksSJKkSgYLkiSpksGCJEmqZLAg\nSZIqGSxIkqRKBguSJKmSwYIkSapksCBJkir9/9+aEM61QjOSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1134f4450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = Series(randn(100),index = range(0,100,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1182fac90>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.hist(bins=20,normed=True,)\n",
    "data.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V/T/Q7Bv4XDfgiruC4H/Tlk/zm0PEo1CUDDO7FPgLcB/QD1gATDGztCoWOQX4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\nK7es5A9D/+A7ijQUX/eEj0f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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118304b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.hist?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
